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About the tools
Make, the platform formerly called Integromat, is visual workflow automation for people who want to see their logic, not just trust it. You build what Make calls scenarios by dragging modules onto a canvas and wiring them together, so a form submission can flow into a CRM, trigger an email, update a spreadsheet, and ping a Slack channel, all laid out in front of you like a flowchart. That visual approach is the whole appeal: instead of a hidden if-this-then-that rule, you watch the data move from step to step.
Where simpler tools stop at connecting two apps, Make keeps going. You can add routers to branch a workflow, filters to control what passes through, iterators to loop over data, and error handlers to catch failures, which lets you build complex automations that would be impossible in a one-trigger tool. For the cases where the visual modules run out, a code app lets you drop in JavaScript or Python.
Over the past couple of years Make has woven AI deep into the product. There are native AI agents, a built-in AI provider available on every plan, and connections to hundreds of AI apps, so a scenario can call a model, weigh the result, and act on it rather than just shuffling data. It connects to thousands of apps and APIs, and its operation-based pricing is consistently cheaper than the per-task model of the obvious competitor, which is a big part of why much of the automation community has moved here. The trade-off is a real learning curve, since the power comes with concepts like data mapping and error handling that take time to absorb.
FUNCTION
Operations
Category
Automation
Pricing model
Free Tier
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Their features
Make is built around the scenario, a visual canvas where each module is an app action or a piece of logic and the connections between them define how data flows.
The building blocks are what set it apart. Beyond simple app-to-app triggers, you get routers to split a workflow down multiple paths, filters to decide what continues, iterators and aggregators to loop over and combine data, and error handlers to manage failures gracefully. Data mapping lets you pass and transform fields between steps with real precision, and a code app supports JavaScript and Python for anything the prebuilt modules cannot do. There are also webhooks, HTTP modules, and full API access for talking to services without a dedicated connector.
Connectivity spans thousands of apps across marketing, sales, operations, finance, and IT, usually with deeper field-level support than lighter tools offer. On the AI side, Make includes native AI agents and an AI toolkit, a built-in AI provider on all plans, the option to bring your own model keys on higher tiers, and connections to hundreds of AI apps, so intelligence sits inside a scenario rather than bolted on.
Pricing is credit-based, measured by the operations your scenarios run. The Free plan gives 1,000 operations a month on two active scenarios, and it is a permanent tier rather than a trial. Core starts at $9 a month for 10,000 operations with unlimited scenarios and shorter run intervals, Pro at $16 adds priority execution and better debugging, Teams at $29 adds collaboration, and Enterprise is custom with security and governance controls. Unused operations can roll over, and there are no surprise overage charges.
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Best for
Make is for people and teams who want serious automation power without writing much code, and who are willing to learn a visual builder to get it. Marketing agencies running multi-client workflows, ops teams automating onboarding and internal processes, and technically curious operators who need custom API connections all tend to land on it. If your automations involve more than three branches or any real data transformation, Make handles that gracefully where simpler tools fall over.
The clearest fits are complex, multi-step workflows where you need conditional logic and visibility into what each run did, and cost-sensitive teams moving off a per-task tool, since Make's operation pricing usually works out several times cheaper for equivalent work. The free plan is a real starting point for testing how it consumes operations before you pay.
Where to think twice: this is not the tool for someone who wants a one-click recipe and never wants to think about it again. The learning curve is real, debugging and error messages can be confusing, and support on lower plans draws frequent complaints. The credit model also rewards attention, since a workflow can burn more operations than you expect, so plan your triggers carefully. And if you run very high volume or heavily AI-driven automations, a self-hosted option like n8n can be cheaper at scale and stronger on native AI nodes. Choose Make when you want hosted, visual automation that can grow into real complexity, and you have someone willing to build and maintain it.
Note: This page was generated partially with AI and reviewed by a human. Errors may occur. We don't take responsibility for the tools' functionality, ethics, or business practices, and features may change after our last update. This information is provided for educational purposes only; how each tool is used remains the sole responsibility of its provider.
Information on this page is accurate as of last edit date:
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